



'''simple docstring'''

import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Any	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[str]	=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

SCREAMING_SNAKE_CASE__     :						int	=  {
    '''vocab_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json'''},
    '''merges_file''': {'''ctrl''': '''https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt'''},
}

SCREAMING_SNAKE_CASE__     :						Tuple	=  {
    '''ctrl''': 2_5_6,
}

SCREAMING_SNAKE_CASE__     :						Optional[int]	=  {
    '''Pregnancy''': 1_6_8_6_2_9,
    '''Christianity''': 7_6_7_5,
    '''Explain''': 1_0_6_4_2_3,
    '''Fitness''': 6_3_4_4_0,
    '''Saving''': 6_3_1_6_3,
    '''Ask''': 2_7_1_7_1,
    '''Ass''': 9_5_9_8_5,
    '''Joke''': 1_6_3_5_0_9,
    '''Questions''': 4_5_6_2_2,
    '''Thoughts''': 4_9_6_0_5,
    '''Retail''': 5_2_3_4_2,
    '''Feminism''': 1_6_4_3_3_8,
    '''Writing''': 1_1_9_9_2,
    '''Atheism''': 1_9_2_2_6_3,
    '''Netflix''': 4_8_6_1_6,
    '''Computing''': 3_9_6_3_9,
    '''Opinion''': 4_3_2_1_3,
    '''Alone''': 4_4_9_6_7,
    '''Funny''': 5_8_9_1_7,
    '''Gaming''': 4_0_3_5_8,
    '''Human''': 4_0_8_8,
    '''India''': 1_3_3_1,
    '''Joker''': 7_7_1_3_8,
    '''Diet''': 3_6_2_0_6,
    '''Legal''': 1_1_8_5_9,
    '''Norman''': 4_9_3_9,
    '''Tip''': 7_2_6_8_9,
    '''Weight''': 5_2_3_4_3,
    '''Movies''': 4_6_2_7_3,
    '''Running''': 2_3_4_2_5,
    '''Science''': 2_0_9_0,
    '''Horror''': 3_7_7_9_3,
    '''Confession''': 6_0_5_7_2,
    '''Finance''': 1_2_2_5_0,
    '''Politics''': 1_6_3_6_0,
    '''Scary''': 1_9_1_9_8_5,
    '''Support''': 1_2_6_5_4,
    '''Technologies''': 3_2_5_1_6,
    '''Teenage''': 6_6_1_6_0,
    '''Event''': 3_2_7_6_9,
    '''Learned''': 6_7_4_6_0,
    '''Notion''': 1_8_2_7_7_0,
    '''Wikipedia''': 3_7_5_8_3,
    '''Books''': 6_6_6_5,
    '''Extract''': 7_6_0_5_0,
    '''Confessions''': 1_0_2_7_0_1,
    '''Conspiracy''': 7_5_9_3_2,
    '''Links''': 6_3_6_7_4,
    '''Narcissus''': 1_5_0_4_2_5,
    '''Relationship''': 5_4_7_6_6,
    '''Relationships''': 1_3_4_7_9_6,
    '''Reviews''': 4_1_6_7_1,
    '''News''': 4_2_5_6,
    '''Translation''': 2_6_8_2_0,
    '''multilingual''': 1_2_8_4_0_6,
}






def 				a  (     UpperCamelCase_    :    Union[str, Any]			)       ->	Tuple:
		snake_case__	     =set()
		snake_case__	     =word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				snake_case__	     =char

		snake_case__	     =set(UpperCamelCase_			)
		return pairs


class 							a__( snake_case__     ):
			a_	: Union[str, Any]									=      VOCAB_FILES_NAMES
			a_	: Optional[Any]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      CONTROL_CODES






			def __init__(     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase="<unk>"					,   **_UpperCAmelCase						)							->							Optional[Any]:
					super().__init__(unk_token=_UpperCAmelCase					,   **_UpperCAmelCase						)

					with open(_UpperCAmelCase					,   encoding='utf-8'						) as vocab_handle:
							snake_case__	     =json.load(_UpperCAmelCase						)
					snake_case__	     ={v: k for k, v in self.encoder.items()}
					with open(_UpperCAmelCase					,   encoding='utf-8'						) as merges_handle:
							snake_case__	     =merges_handle.read().split('\n'						)[1:-1]
					snake_case__	     =[tuple(merge.split()						) for merge in merges]
					snake_case__	     =dict(zip(_UpperCAmelCase					,   range(len(_UpperCAmelCase						)						)						)						)
					snake_case__	     ={}






			@property
			def 						_lowercase  (     self						)							->							Tuple:
					return len(self.encoder						)






			def 						_lowercase  (     self						)							->							Dict:
					return dict(self.encoder					,   **self.added_tokens_encoder						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Dict:
					if token in self.cache:
							return self.cache[token]
					snake_case__	     =tuple(_UpperCAmelCase						)
					snake_case__	     =tuple(list(word[:-1]						) + [word[-1] + '</w>']						)
					snake_case__	     =get_pairs(_UpperCAmelCase						)

					if not pairs:
							return token

					while True:
							snake_case__	     =min(_UpperCAmelCase					,   key=lambda _UpperCAmelCase						: self.bpe_ranks.get(_UpperCAmelCase					,   float('inf'						)						)						)
							if bigram not in self.bpe_ranks:
									break
							snake_case__     ,	snake_case__	     =bigram
							snake_case__	     =[]
							snake_case__	     =0
							while i < len(_UpperCAmelCase						):
									try:
											snake_case__	     =word.index(_UpperCAmelCase					,   _UpperCAmelCase						)
									except ValueError:
											new_word.extend(word[i:]						)
											break
									else:
											new_word.extend(word[i:j]						)
											snake_case__	     =j

									if word[i] == first and i < len(_UpperCAmelCase						) - 1 and word[i + 1] == second:
											new_word.append(first + second						)
											i += 2
									else:
											new_word.append(word[i]						)
											i += 1
							snake_case__	     =tuple(_UpperCAmelCase						)
							snake_case__	     =new_word
							if len(_UpperCAmelCase						) == 1:
									break
							else:
									snake_case__	     =get_pairs(_UpperCAmelCase						)
					snake_case__	     ='@@ '.join(_UpperCAmelCase						)
					snake_case__	     =word[:-4]
					snake_case__	     =word
					return word






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Union[str, Any]:
					snake_case__	     =[]

					snake_case__	     =re.findall(r'\S+\n?'					,   _UpperCAmelCase						)

					for token in words:
							split_tokens.extend(list(self.bpe(_UpperCAmelCase						).split(' '						)						)						)
					return split_tokens






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							int:
					return self.encoder.get(_UpperCAmelCase					,   self.encoder.get(self.unk_token						)						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Tuple:
					return self.decoder.get(_UpperCAmelCase					,   self.unk_token						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =' '.join(_UpperCAmelCase						).replace('@@ '					,   ''						).strip()
					return out_string






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

					with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as f:
							f.write(json.dumps(self.encoder					,   indent=2					,   sort_keys=_UpperCAmelCase					,   ensure_ascii=_UpperCAmelCase						) + '\n'						)

					snake_case__	     =0
					with open(_UpperCAmelCase					,   'w'					,   encoding='utf-8'						) as writer:
							writer.write('#version: 0.2\n'						)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,   key=lambda _UpperCAmelCase						: kv[1]						):
									if index != token_index:
											logger.warning(
											    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
											    ' Please check that the tokenizer is not corrupted!'						)
											snake_case__	     =token_index
									writer.write(' '.join(_UpperCAmelCase						) + '\n'						)
									index += 1

					return vocab_file, merge_file

			# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
			#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
			#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
			#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
			#     return ''.join(tokens_generated_so_far)









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    list[str] | None = None      ,					UpperCamelCase_    :    dict[str, float] | None = None      ,					UpperCamelCase_    :    bool = False      ,					)       ->	tuple[int, float, str]:
		snake_case__	     =cipher_alphabet or [chr(UpperCamelCase_			) for i in range(97      ,					123			)]

		# If the argument is None or the user provided an empty dictionary
		if not frequencies_dict:
				# Frequencies of letters in the english language (how much they show up)
				snake_case__	     ={
				    'a': 0.0_8_4_9_7,
				    'b': 0.0_1_4_9_2,
				    'c': 0.0_2_2_0_2,
				    'd': 0.0_4_2_5_3,
				    'e': 0.1_1_1_6_2,
				    'f': 0.0_2_2_2_8,
				    'g': 0.0_2_0_1_5,
				    'h': 0.0_6_0_9_4,
				    'i': 0.0_7_5_4_6,
				    'j': 0.0_0_1_5_3,
				    'k': 0.0_1_2_9_2,
				    'l': 0.0_4_0_2_5,
				    'm': 0.0_2_4_0_6,
				    'n': 0.0_6_7_4_9,
				    'o': 0.0_7_5_0_7,
				    'p': 0.0_1_9_2_9,
				    'q': 0.0_0_0_9_5,
				    'r': 0.0_7_5_8_7,
				    's': 0.0_6_3_2_7,
				    't': 0.0_9_3_5_6,
				    'u': 0.0_2_7_5_8,
				    'v': 0.0_0_9_7_8,
				    'w': 0.0_2_5_6_0,
				    'x': 0.0_0_1_5_0,
				    'y': 0.0_1_9_9_4,
				    'z': 0.0_0_0_7_7,
				}
		else:
				# Custom frequencies dictionary
				snake_case__	     =frequencies_dict

		if not case_sensitive:
				snake_case__	     =ciphertext.lower()

		# Chi squared statistic values
		snake_case__	     ={}

		# cycle through all of the shifts
		for shift in range(len(UpperCamelCase_			)			):
				snake_case__	     =''

				# decrypt the message with the shift
				for letter in ciphertext:
						try:
								# Try to index the letter in the alphabet
								snake_case__	     =(alphabet_letters.index(letter.lower()			) - shift) % len(
								    UpperCamelCase_			)
								decrypted_with_shift += (
								    alphabet_letters[new_key].upper()
								    if case_sensitive and letter.isupper()
								    else alphabet_letters[new_key]
								)
						except ValueError:
								# Append the character if it isn't in the alphabet
								decrypted_with_shift += letter

				snake_case__	     =0.0

				# Loop through each letter in the decoded message with the shift
				for letter in decrypted_with_shift:
						if case_sensitive:
								snake_case__	     =letter.lower()
								if letter in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.lower().count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value
						else:
								if letter.lower() in frequencies:
										# Get the amount of times the letter occurs in the message
										snake_case__	     =decrypted_with_shift.count(UpperCamelCase_			)

										# Get the excepcted amount of times the letter should appear based
										# on letter frequencies
										snake_case__	     =frequencies[letter] * occurrences

										# Complete the chi squared statistic formula
										snake_case__	     =((occurrences - expected) ** 2) / expected

										# Add the margin of error to the total chi squared statistic
										chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
				snake_case__	     =(
				    chi_squared_statistic,
				    decrypted_with_shift,
				)

		# Get the most likely cipher by finding the cipher with the smallest chi squared
		# statistic
		def chi_squared_statistic_values_sorting_key(UpperCamelCase_    :    int			) -> tuple[float, str]:
				return chi_squared_statistic_values[key]

		snake_case__	     =min(
		    UpperCamelCase_      ,					key=UpperCamelCase_      ,					)

		# Get all the data from the most likely cipher (key, decoded message)
		(
		    (
		    snake_case__
		)     ,	(
		    snake_case__
		)     ,	
		)	     =chi_squared_statistic_values[most_likely_cipher]

		# Return the data on the most likely shift
		return (
		    most_likely_cipher,
		    most_likely_cipher_chi_squared_value,
		    decoded_most_likely_cipher,
		)




